Generating Training Data For A Conversational Query Response System

Abstract

Training tuples including text and a question and answer corresponding to the text are input to a machine learning algorithm, such as a deep neural network. A Q&A model is obtained that outputs questions and answers given an input text. The training tuples may be obtained from standardized test such that the text is a question prompt and the questions and answers are based on the prompt. Raw text is input to the Q&A model to obtain second training tuples including a question and an answer. An NLU model is trained according to the second training tuples. The NLU model may then be installed on a consumer device, which will then use the model to respond to conversational queries and provide an appropriate response.

What is claimed is: 
     
         1 . A method for training a query-response model for use in a vehicle, the method comprising, by a computer system:
 training a first model using a first plurality of tuples each including text, a question, and an answer;   processing unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and   training a second model using the second plurality of tuples.   
     
     
         2 . The method of  claim 1 , further comprising loading the second model onto a consumer computing device. 
     
     
         3 . The method of  claim 2 , wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle. 
     
     
         4 . The method of  claim 3 , further comprising:
 programming the IVI system to receive a query, input the query to the second model, and output a response according to the second model.   
     
     
         5 . The method of  claim 3 , further comprising:
 programming the IVI system to input voice queries to the second model and output a response to the query according to the second model.   
     
     
         6 . The method of  claim 1 , wherein the first model is a deep neural network (DNN) model. 
     
     
         7 . The method of  claim 1 , wherein the second model is a deep neural network (DNN) model. 
     
     
         8 . The method of  claim 1 , wherein processing the unstructured data using the first model comprises:
 pre-processing, by the computer system, the unstructured data to identify a feature set from within the unstructured data; and   inputting, by the computer system, the feature set to the first model.   
     
     
         9 . The method of  claim 1 , wherein the unstructured data comprises at least one of text and images. 
     
     
         10 . The method of  claim 1 , wherein the first plurality of tuples are derived from test preparation materials for students. 
     
     
         11 . A system for training a query-response model comprising:
 a first machine learning module including at least one processing device, the machine learning module programmed to:
 train a first model using a first plurality of tuples each including text, a question, and an answer; 
 process unstructured data using the first model to obtain a second plurality of tuples each including a question and an answer; and 
   a second machine learning module programmed to train a second model using the second plurality of tuples, the second model being a natural language understanding (NLU) model.   
     
     
         12 . The system of  claim 11 , wherein the second machine learning module is further programmed to cause the one or more processors to load the second model onto a consumer computing device. 
     
     
         13 . The system of  claim 12 , wherein the consumer computing device is an in-vehicle infotainment (IVI) system mounted in a vehicle. 
     
     
         14 . The system of  claim 13 , wherein the second machine learning module is further programmed to program the IVI system to receive a query, input the query to the second model, and output a response according to the second model. 
     
     
         15 . The system of  claim 13  wherein the second machine learning module is further programmed to program the IVI system, to input voice queries to the second model and output a response to the query according to the second model. 
     
     
         16 . The system of  claim 11 , wherein the first model is a deep neural network (DNN) model. 
     
     
         17 . The system of  claim 11 , wherein the second model is a deep neural network (DNN) model. 
     
     
         18 . The system of  claim 11 , wherein the first machine learning module is further programmed to process the unstructured data using the first model by:
 pre-processing the unstructured data to identify a feature set from within the unstructured data; and   inputting the feature set to the first model.   
     
     
         19 . The system of  claim 11 , wherein the unstructured data comprises at least one of text and images. 
     
     
         20 . The system of  claim 11 , wherein the first machine learning module is further programmed to derive the first plurality of tuples from test preparation materials for students.